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An introduction to state-space models, particle filters, and Sequential Monte Carlo samplers - Part 1
Multi angle
Authors : Chopin, Nicolas (Author of the conference)
CIRM (Publisher )
CIRM (Publisher )
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Abstract :
This course will provide a general introduction to SMC algorithms, from basic particle filters and their uses in state-space (hidden Markov) modelling in various areas, to more advanced algorithms such as SMC samplers, which may be used to sample from one, or several target distributions. The course will cover “a bit of everything”: theory (using Feynman-Kac models as a general framework), methodology (how to construct better algorithms in practice), implementation (examples in Python based on the library particles will be showcased), and applications.
Keywords : sequential Monte Carlo; particle filtering; state-space models; hidden Markov models; importance sampling; resampling; Markov chain Monte Carlo; Bayesian inference; maximum likelihood estimation
MSC Codes : Keywords : sequential Monte Carlo; particle filtering; state-space models; hidden Markov models; importance sampling; resampling; Markov chain Monte Carlo; Bayesian inference; maximum likelihood estimation
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Information on the Video
Film maker : Récanzone, LucaLanguage : English Available date : 27/11/2023 Conference Date : 30/10/2023 Subseries : Research School arXiv category : Computation Mathematical Area(s) : Numerical Analysis & Scientific Computing ; Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 01:51:01 Targeted Audience : Researchers ; Graduate Students ; Doctoral Students, Post-Doctoral Students Download : https://videos.cirm-math.fr/2023-10-30-chopin_1.mp4 
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Information on the Event
Event Title : Autumn school in Bayesian Statistics / École d'automne en statistique bayésienneEvent Organizers : Arbel, Julyan ; Etienne, Marie-Pierre ; Filippi, Sarah ; Kon Kam King, Guillaume ; Ryder, Robin ; Ancelet, Sophie ; Bardenet, Rémi ; Bonnet, Anna ; Jacob, Pierre Dates : 30/10/2023 - 03/11/2023 Event Year : 2023 Event URL : https://conferences.cirm-math.fr/2881.html
Citation Data
DOI : 10.24350/CIRM.V.20107303Cite this video as: Chopin, Nicolas (2023). An introduction to state-space models, particle filters, and Sequential Monte Carlo samplers - Part 1. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.20107303 URI : http://dx.doi.org/10.24350/CIRM.V.20107303 |
See Also
- [Multi angle] An introduction to state-space models, particle filters, and Sequential Monte Carlo samplers - Part 2 / Author of the conference Chopin, Nicolas.
- [Multi angle] Bayesian causal inference: a potential outcome perspective with applications to intermediate variables / Author of the conference Mealli, Fabrizia.
- [Multi angle] Merging rate of opinions via optimal transport on random measures / Author of the conference Catalano, Marta.
- [Multi angle] Monte Carlo guided Diffusion for Bayesian linear inverse problems / Author of the conference Le Corff, Sylvain.
Bibliography
- CHOPIN, Nicolas, PAPASPILIOPOULOS, Omiros, et al. An introduction to sequential Monte Carlo. New York : Springer, 2020. - https://doi.org/10.1007/978-3-030-47845-2
Imagette Video
